联邦多模态学习框架，提升动态环境下的可信与可解释性。
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
- 融合联邦学习与可解释多模态推理，统一跨模态一致性验证。
- 在视觉语言任务中提升准确率，降低对抗攻击与虚假关联风险。
- 支持动态客户端参与，量化全局模型可靠性，适合高可信场景。
随着人工智能系统在真实环境中广泛应用，视觉、语言、音频等多模态数据的融合带来了前所未有的机遇与挑战，尤其在实现可信智能方面。本文提出一种新框架 FedMM-X（联邦多模态可解释智能），将联邦学习与可解释多模态推理相结合，以应对去中心化动态环境中的数据异构性、模态不平衡和分布外泛化问题。该框架引入跨模态一致性检测、客户端级可解释机制以及动态信任校准策略。在包含视觉-语言任务的联邦多模态基准上进行严格评估，结果表明其在准确率和可解释性方面均有提升，同时降低了对对抗样本和虚假相关性的脆弱性。此外，提出一种新颖的信任得分聚合方法，用于量化动态客户端参与下全局模型的可靠性。研究为构建鲁棒、可解释且符合社会伦理的现实世界AI系统提供了路径。
原文摘要 · Abstract (English)
As artificial intelligence systems increasingly operate in Real-world environments, the integration of multi-modal data sources such as vision, language, and audio presents both unprecedented opportunities and critical challenges for achieving trustworthy intelligence. In this paper, we propose a novel framework that unifies federated learning with explainable multi-modal reasoning to ensure trustworthiness in decentralized, dynamic settings. Our approach, called FedMM-X (Federated Multi-Modal Explainable Intelligence), leverages cross-modal consistency checks, client-level interpretability mechanisms, and dynamic trust calibration to address challenges posed by data heterogeneity, modality imbalance, and out-of-distribution generalization. Through rigorous evaluation across federated multi-modal benchmarks involving vision-language tasks, we demonstrate improved performance in both accuracy and interpretability while reducing vulnerabilities to adversarial and spurious correlations. Further, we introduce a novel trust score aggregation method to quantify global model reliability under dynamic client participation. Our findings pave the way toward developing robust, interpretable, and socially responsible AI systems in Real-world environments.
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